A seismic to simulation unconventional workflow using automated fault-detection attributes

2017 ◽  
Vol 5 (3) ◽  
pp. SJ41-SJ48 ◽  
Author(s):  
Jesse Lomask ◽  
Luisalic Hernandez ◽  
Veronica Liceras ◽  
Amit Kumar ◽  
Anna Khadeeva
Keyword(s):  
Seismic Data ◽  
Spatial Information ◽  
Spatial Location ◽  
Statistical Information ◽  
Natural Fracture ◽  
Fracture Networks ◽  
Data Set ◽  
Fault Indicator ◽  
Well Data ◽  
Additional Constraints

Natural fracture networks (NFNs) are used in unconventional reservoir simulators to model pressure and saturation changes in fractured rocks. These fracture networks are often derived from well data or well data combined with a variety of seismic-derived attributes to provide spatial information away from the wells. In cases in which there is a correlation between faults and fractures, the use of a fault indicator can provide additional constraints on the spatial location of the natural fractures. We use a fault attribute based on fault-oriented semblance as a secondary conditioner for the generation of NFNs. In addition, the distribution of automatically extracted faults from the fault-oriented semblance is used to augment the well-derived statistics for natural fracture generation. Without the benefit of this automated fault-extraction solution, to manually extract the fault-statistical information from the seismic data would be prohibitively tedious and time consuming. Finally, we determine, on a 3D field unconventional data set, that the use of fault-oriented semblance results in simulations that are significantly more geologically reasonable.

Can fracture orientation and intensity be detected from seismic data? Woodford Formation, Anadarko Basin, Oklahoma investigation

2019 ◽  
Vol 38 (2) ◽  
pp. 144-150 ◽  
Author(s):  
Marianne Rauch-Davies ◽  
David Langton ◽  
Michael Bradshaw ◽  
Allon Bartana ◽  
Dan Kosloff ◽  
...  
Keyword(s):  
Seismic Data ◽  
Subsurface Layer ◽  
Azimuthal Velocity ◽  
Natural Fracture ◽  
Woodford Shale ◽  
Data Set ◽  
Anadarko Basin ◽  
Fracture Orientation ◽  
Anisotropic Elastic ◽  
Elastic Simulation

With readily available wide-azimuth, onshore, 3D seismic data, the search for attributes utilizing the azimuthal information is ongoing. Theoretically, in the presence of ordered fracturing, the seismic wavefront shape changes from spherical to nonspherical with the propagation velocity being faster parallel to the fracturing and slower perpendicular to the fracture direction. This concept has been adopted and is used to map fracture direction and density within unconventional reservoirs. More specifically, azimuthal variations in normal moveout velocity or migration velocity are often used to infer natural fracture orientation. Analyses of recent results have called into question whether azimuthal velocity linked to intrinsic azimuthal velocity variations can actually be detected from seismic data. By use of 3D orthorhombic anisotropic elastic simulation, we test whether fracture orientation and intensity can be detected from seismic data. We construct two subsurface models based on interpreted subsurface layer structure of the Anadarko Basin in Oklahoma. For the first model, the material parameters in the layers are constant vertically transverse isotropic (VTI) in all intervals. The second model was constructed the same way as the base model for all layers above the Woodford Shale Formation. For the shale layer, orthorhombic properties were introduced. In addition, a thicker wedge layer was added below the shale layer. Using the constructed model, synthetic seismic data were produced by means of 3D anisotropic elastic simulation resulting in two data sets: VTI and orthorhombic. The simulated data set was depth migrated using the VTI subsurface model. After migration, the residual moveouts on the migrated gathers were analyzed. The analysis of the depth-migrated model data indicates that for the typical layer thicknesses of the Woodford Shale layer in the Anadarko Basin, observed and modeled percentage of anisotropy and target depth, the effect of intrinsic anisotropy is too small to be detected in real seismic data.

Fracture detection via correlating P-wave amplitude variation with offset and azimuth analysis and well data in eastern central Saudi Arabia

2017 ◽  
Vol 5 (4) ◽  
pp. T531-T544
Author(s):  
Ali H. Al-Gawas ◽  
Abdullatif A. Al-Shuhail
Keyword(s):  
Saudi Arabia ◽  
Seismic Data ◽  
Production Data ◽  
Fracture Zones ◽  
Fracture Detection ◽  
Amplitude Variation ◽  
Data Set ◽  
Amplitude Variation With Offset ◽  
Well Data ◽  
Patch Geometry

The late Carboniferous clastic Unayzah-C in eastern central Saudi Arabia is a low-porosity, possibly fractured reservoir. Mapping the Unayzah-C is a challenge due to the low signal-to-noise ratio (S/N) and limited bandwidth in the conventional 3D seismic data. A related challenge is delineating and characterizing fracture zones within the Unayzah-C. Full-azimuth 3D broadband seismic data were acquired using point receivers, low-frequency sweeps down to 2 Hz, and 6 km patch geometry. The data indicate significant enhancement in continuity and resolution of the reflection data, leading to improved mapping of the Unayzah-C. Because the data set has a rectangular patch geometry with full inline offsets to 6000 m, using amplitude variation with offset and azimuth (AVOA) may be effective to delineate and characterize fracture zones within Unayzah-A and Unayzah-C. The study was undertaken to determine the improvement of wide-azimuth seismic data in fracture detection in clastic reservoirs. The results were validated with available well data including borehole images, well tests, and production data in the Unayzah-A. There are no production data or borehole images within the Unayzah-C. For validation, we had to refer to a comparison of alternative seismic fracture detection methods, mainly curvature and coherence. Anisotropy was found to be weak, which may be due to noise, clastic lithology, and heterogeneity of the reservoirs, in both reservoirs except for along the western steep flank of the study area. These may correspond to some north–south-trending faults suggested by circulation loss and borehole image data in a few wells. The orientation of the long axis of the anisotropy ellipses is northwest–southeast, and it is not in agreement with the north–south structural trend. No correlation was found among the curvature, coherence, and AVOA in Unayzah-A or Unayzah-C. Some possible explanations for the low correlation between the AVOA ellipticity and the natural fractures are a noisy data set, overburden anisotropy, heterogeneity, granulation seams, and deformation.

Building tilted transversely isotropic depth models using localized anisotropic tomography with well information

Geophysics ◽  
2010 ◽  
Vol 75 (4) ◽  
pp. D27-D36 ◽  
Author(s):  
Andrey Bakulin ◽  
Marta Woodward ◽  
Dave Nichols ◽  
Konstantin Osypov ◽  
Olga Zdraveva
Keyword(s):  
Seismic Data ◽  
Model Building ◽  
Symmetry Axis ◽  
Transverse Isotropy ◽  
Synthetic Data ◽  
Rock Physics ◽  
Transversely Isotropic ◽  
Data Set ◽  
Anisotropic Models ◽  
Well Data

Tilted transverse isotropy (TTI) is increasingly recognized as a more geologically plausible description of anisotropy in sedimentary formations than vertical transverse isotropy (VTI). Although model-building approaches for VTI media are well understood, similar approaches for TTI media are in their infancy, even when the symmetry-axis direction is assumed known. We describe a tomographic approach that builds localized anisotropic models by jointly inverting surface-seismic and well data. We present a synthetic data example of anisotropic tomography applied to a layered TTI model with a symmetry-axis tilt of 45 degrees. We demonstrate three scenarios for constraining the solution. In the first scenario, velocity along the symmetry axis is known and tomography inverts for Thomsen’s [Formula: see text] and [Formula: see text] parame-ters. In the second scenario, tomography inverts for [Formula: see text], [Formula: see text], and velocity, using surface-seismic data and vertical check-shot traveltimes. In contrast to the VTI case, both these inversions are nonunique. To combat nonuniqueness, in the third scenario, we supplement check-shot and seismic data with the [Formula: see text] profile from an offset well. This allows recovery of the correct profiles for velocity along the symmetry axis and [Formula: see text]. We conclude that TTI is more ambiguous than VTI for model building. Additional well data or rock-physics assumptions may be required to constrain the tomography and arrive at geologically plausible TTI models. Furthermore, we demonstrate that VTI models with atypical Thomsen parameters can also fit the same joint seismic and check-shot data set. In this case, although imaging with VTI models can focus the TTI data and match vertical event depths, it leads to substantial lateral mispositioning of the reflections.

scholarly journals Boltzmann Entropy for the Spatial Information of Raster Data

2019 ◽  
Vol 1 ◽  
pp. 1-1 ◽  
Author(s):  
Peichao Gao ◽  
Hong Zhang ◽  
Zhilin Li
Keyword(s):  
Boltzmann Equation ◽  
Information Content ◽  
Shannon Entropy ◽  
Spatial Data ◽  
Spatial Information ◽  
Statistical Information ◽  
Boltzmann Entropy ◽  
Raster Data ◽  
Data Set ◽  
The Boltzmann Equation

<p><strong>Abstract.</strong> Entropy is an important concept that originated in thermodynamics. It is the subject of the famous Second Law of Thermodynamics, which states that “the entropy of a closed system increases continuously and irrevocably toward a maximum” (Huettner 1976, 102) or “the disorder in the universe always increases” (Framer and Cook 2013, 21). Accordingly, it has been widely regarded as an ideal measure of disorder. Its computation can be theoretically performed according to the Boltzmann equation, which was proposed by the Austrian physicist Ludwig Boltzmann in 1872. In practice, however, the Boltzmann equation involves two problems that are difficult to solve, that is the definition of the macrostate of a system and the determination of the number of possible microstates in the microstate. As noted by the American sociologist Kenneth Bailey, “when the notion of entropy is extended beyond physics, researchers may not be certain how to specify and measure the macrostate/microstate relations” (Bailey 2009, 151). As a result, this entropy (also referred to as Boltzmann entropy and thermodynamic entropy) has remained largely at a conceptual level.</p><p> In practice, the widely used entropy is actually proposed by the American mathematician, electrical engineer, and cryptographer Claude Elwood Shannon in 1948, hence the term Shannon entropy. Shannon entropy was proposed to quantify the statistical disorder of telegraph messages in the area of communications. The quantification result was interpreted as the information content of a telegraph message, hence also the term information entropy. This entropy has served as the cornerstone of information theory and was introduced to various fields including chemistry, biology, and geography. It has been widely utilized to quantify the information content of geographic data (or spatial data) in either a vector format (i.e., vector data) or a raster format (i.e., raster data). However, only the statistical information of spatial data can be quantified by using Shannon entropy. The spatial information is ignored by Shannon entropy; for example, a grey image and its corresponding error image share the same Shannon entropy.</p><p> Therefore, considerable efforts have been made to improve the suitability of Shannon entropy for spatial data, and a number of improved Shannon entropies have been put forward. Rather than further improving Shannon entropy, this study introduces a novel strategy, namely shifting back from Shannon entropy to Boltzmann entropy. There are two advantages of employing Boltzmann entropy. First, as previously mentioned, Boltzmann entropy is the ideal, standard measure of disorder or information. It is theoretically capable of quantifying not only the statistical information but also the spatial information of a data set. Second, Boltzmann entropy can serve as the bridge between spatial patterns and thermodynamic interpretations. In this sense, the Boltzmann entropy of spatial data may have wider applications. In this study, Boltzmann entropy is employed to quantify the spatial information of raster data, such as images, raster maps, digital elevation models, landscape mosaics, and landscape gradients. To this end, the macrostate of raster data is defined, and the number of all possible microstates in the macrostate is determined. To demonstrate the usefulness of Boltzmann entropy, it is applied to satellite remote sensing image processing, and a comparison is made between its performance and that of Shannon entropy.</p>

Multidomain analysis and wavefield separation of cross‐well seismic data

Geophysics ◽  
1994 ◽  
Vol 59 (1) ◽  
pp. 27-35 ◽  
Author(s):  
James W. Rector ◽  
Spyros K. Lazaratos ◽  
Jerry M. Harris ◽  
Mark Van Schaack
Keyword(s):  
Seismic Data ◽  
Common Source ◽  
Data Set ◽  
Traveltime Tomography ◽  
Wavefield Separation ◽  
Vsp Data ◽  
Well Data ◽  
Multiple Domains

While cross‐well traveltime tomography can be used to image the subsurface between well pairs, the use of cross‐well reflections is necessary to image at or below the base of wells, where the reservoir unit is often located. One approach to imaging cross‐well reflections is to treat each cross‐well gather as an offset VSP and perform wavefield separation of direct and reflected arrivals prior to stacking or migration. Wavefield separation of direct and reflected arrivals in VSP is accomplished by separating the total wavefield into up and downgoing components. Since reflectors can exist both above and below the borehole wavefield, separation of cross‐well data into up‐ and downgoing components does not achieve separation of direct and reflected arrivals. In our technique, we use moveout filters applied in the domain of common vertical source/receiver offset to extract reflected arrivals from the complex total wavefield of a cross‐well seismic data set. The multiple domains available for filtering and analysis make cross‐well data more akin to multifold surface seismic data, which can also be filtered in multiple domains, rather than typical VSP data, where there is only one domain (common source) in which to filter. Wavefield separation of cross‐well data is shown to be particularly effective against multiples when moveout filters are applied in common‐offset space.

Automated arrival-time picking using a pixel-level network

Geophysics ◽  
2020 ◽  
Vol 85 (5) ◽  
pp. V415-V423
Author(s):  
Yuanyuan Ma ◽  
Siyuan Cao ◽  
James W. Rector ◽  
Zhishuai Zhang
Keyword(s):  
Seismic Data ◽  
Arrival Time ◽  
Spatial Information ◽  
Training Data ◽  
Convolutional Network ◽  
Data Set ◽  
Arrival Times ◽  
Data Volume ◽  
Borehole Seismic ◽  
Arrival Time Picking

Arrival-time picking is an essential step in seismic processing and imaging. The explosion of seismic data volume requires automated arrival-time picking in a faster and more reliable way than existing methods. We have treated arrival-time picking as a binary image segmentation problem and used an improved pixel-wise convolutional network to pick arrival times automatically. Incorporating continuous spatial information in training enables us to preserve the arrival-time correlation between nearby traces, thus helping to reduce the risk of picking outliers that are common in a traditional trace-by-trace picking method. To train the network, we first convert seismic traces into gray-scale images. Image pixels before manually picked arrival times are labeled with zeros, and those after are tagged with ones. After training and validation, the network automatically learns representative features and generates a probability map to predict the arrival time. We apply the network to a field microseismic data set that was not used for training or validation to test the performance of the method. Then, we analyze the effects of training data volume and signal-to-noise ratio on our autopicking method. We also find the difference between 1D and 2D training data with borehole seismic data. Microseismic and borehole seismic data indicate the proposed network can improve efficiency and accuracy over traditional automated picking methods.

Localized anisotropic tomography with well information in VTI media

Geophysics ◽  
2010 ◽  
Vol 75 (5) ◽  
pp. D37-D45 ◽  
Author(s):  
Andrey Bakulin ◽  
Marta Woodward ◽  
Dave Nichols ◽  
Konstantin Osypov ◽  
Olga Zdraveva
Keyword(s):  
Vertical Velocity ◽  
Seismic Data ◽  
Model Building ◽  
Synthetic Data ◽  
Resolution Limit ◽  
Data Set ◽  
Anisotropic Models ◽  
Velocity Models ◽  
First Case ◽  
Well Data

We develop a concept of localized seismic grid tomography constrained by well information and apply it to building vertically transversely isotropic (VTI) velocity models in depth. The goal is to use a highly automated migration velocity analysis to build anisotropic models that combine optimal image focusing with accurate depth positioning in one step. We localize tomography to a limited volume around the well and jointly invert the surface seismic and well data. Well information is propagated into the local volume by using the method of preconditioning, whereby model updates are shaped to follow geologic layers with spatial smoothing constraints. We analyze our concept with a synthetic data example of anisotropic tomography applied to a 1D VTI model. We demonstrate four cases of introducing additionalinformation. In the first case, vertical velocity is assumed to be known, and the tomography inverts only for Thomsen’s [Formula: see text] and [Formula: see text] profiles using surface seismic data alone. In the second case, tomography simultaneously inverts for all three VTI parameters, including vertical velocity, using a joint data set that consists of surface seismic data and vertical check-shot traveltimes. In the third and fourth cases, sparse depth markers and walkaway vertical seismic profiling (VSP) are used, respectively, to supplement the seismic data. For all four examples, tomography reliably recovers the anisotropic velocity field up to a vertical resolution comparable to that of the well data. Even though walkaway VSP has the additional dimension of angle or offset, it offers no further increase in this resolution limit. Anisotropic tomography with well constraints has multiple advantages over other approaches and deserves a place in the portfolio of model-building tools.

Integrated interpretation of gravity, magnetic, seismic, and well data to image volcanic units for oil-gas exploration in the eastern Junggar Basin, northwest China

2020 ◽  
Vol 8 (4) ◽  
pp. SS113-SS127
Author(s):  
Kaijun Xu ◽  
Yaoguo Li
Keyword(s):  
Seismic Data ◽  
Volcanic Rocks ◽  
Junggar Basin ◽  
Magnetic Data ◽  
Magnetic Method ◽  
Data Set ◽  
Amplitude Inversion ◽  
Gravity And Magnetic ◽  
Integrated Interpretation ◽  
Well Data

We carried out a multigeophysical data joint interpretation to image volcanic units in an area where seismic imaging is difficult due to complicated and variable volcanic lithology. The gravity and magnetic methods can be effective in imaging the volcanic units because volcanic rocks are often strongly magnetic and have large density contrasts. Gravity and magnetic data have good lateral resolution, but they are faced with challenges in defining the depth extent. Although seismic data make for poor imaging in volcanic rocks, they can provide a reliable stratigraphic structure above volcanic rocks to improve the vertical resolution of the gravity and magnetic method. We have developed an integrated interpretation method that combines the advantages of seismic, gravity, magnetic, and well data to generate a 3D quasigeology model to image volcanic units. We first use seismic data to obtain the stratigraphic boundaries, and then we apply an anomaly stripping method based on a seismic-derived structure to extract residual gravity and magnetic anomaly produced by volcanic rocks. We further perform the 3D gravity and magnetic amplitude inversion to recover the distribution of the density and effective susceptibility. We perform geology differentiation using the inverted density and effective magnetic susceptibility to identify the spatial distribution of four groups of volcanic units. The results show that the integrated interpretation of multigeophysical data can significantly decrease the uncertainty associated with any single data set and yield more reliable imaging of lateral and vertical distribution of volcanic rocks.

Ordovician glacial paleogeography: Integration of seismic spectral decomposition, well sedimentological data, and glacial modern analogs in the Murzuq Basin, Libya

2019 ◽  
Vol 7 (2) ◽  
pp. T383-T408 ◽  
Author(s):  
Francisco J. Bataller ◽  
Neil McDougall ◽  
Andrea Moscariello
Keyword(s):  
Seismic Data ◽  
Spectral Decomposition ◽  
Depositional Environments ◽  
Optimum Frequency ◽  
Data Set ◽  
Glacial Sediments ◽  
Depositional System ◽  
Significant Difference ◽  
Different Depths ◽  
Well Data

Ancient glacial sediments form major hydrocarbon plays in several parts of the world; most notably, North Africa, Latin America, and the Middle East. We have described a methodology for reconstructing broad-scale paleogeographies in just such a depositional system, using an extensive subsurface data set from the uppermost Ordovician glacial sediments of the Murzuq Basin of southwest Libya. Our workflow begins with the analysis of a large, high-quality 3D seismic data set, to understand the frequency content. Subsequently, optimum frequency bands are extracted, after applying spectral decomposition, and then recombined into an R (red) G (green) B (blue) blended cube. This volume is then treated as an image within which paleomorphological features can be distinguished and compared with modern glacial analogs. Mapping at different depths (time slices) of these features is then tied, by integration with core and image-log sedimentology, to specific depositional environments defined within the framework of a facies scheme developed using the well data and published outcrop studies. These depositional environments are extrapolated into areas with little or no well data using the spectral decomposition as a framework, always taking into account the significant difference in vertical resolution between the seismic data set and core-scale descriptions. The result of this methodology is a set of calibrated maps, at three different time depths (two-way time travel), indicating paleogeographic reconstructions of the glacial depositional environments in the study area and the evolution through time (at different depths/time slices 2D + 1) of these glacial settings.

Reconstruction of 2D Seismic Wavefields from Nonuniformly Sampled Sources

2020 ◽  
Vol 2020 (14) ◽  
pp. 307-1-307-6
Author(s):  
Laura Galvis ◽  
Juan M. Ramírez ◽  
Edwin Vargas ◽  
Ofelia Villarreal ◽  
William Agudelo ◽  
...  
Keyword(s):  
Seismic Data ◽  
Curvelet Transform ◽  
Real Data ◽  
Spatial Location ◽  
Seismic Survey ◽  
Signal Recovery ◽  
3D Seismic ◽  
Regular Grid ◽  
Data Set ◽  
Recovery Algorithm

In a 3D seismic survey, the source sampling in a regular grid is commonly limited by economic costs, geological constraints, and environmental challenges. This non-uniform sampling cannot be ignored since the lack of regularity leads to incomplete seismic data with missing 2D wavefields. Notice that the postprocessing tasks have been developed under the assumption that 3D seismic data are obtained from a regular sampling. Therefore, signal recovery from incomplete data becomes a crucial step in the seismic imaging processing flow. In this work, we propose a pre-processing step that includes the nonuniformly acquired wavefields in a finer regular grid, such that shot gathers are stacked considering the actual spatial location of the sources. Then, based on the 3D curvelet transform, a sparse signal recovery algorithm that considers an interpolation operator is employed in order to reconstruct the missing wavefields in a regular grid. The performance of the proposed seismic reconstruction approach is evaluated on a real data set.

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